The Bayesian theory is used to model the uncertainty of networks but the computational complexity makes it prohibitive for high dimensional problems like image classification. However, existing deep learning models can be cast as Bayesian models without changing the models [1]}. The dropout layer which is used only during training is enabled during the testing and the model is run several times for each test sample. This results in an approximate posterior probability distribution for each image. This uncertainty information can improve the diagnostic performance as shown in a study for diagnosing diabetic retinopathy from retinal fundus image [2]}. It helped identify unfamiliar images that should be referred to a clinician instead of making an ambiguous choice.
